Toward Smart Logistics: Hybrization of Intelligence Techniques of Machine Learning and Multi-Criteria Decision-Making in Logistics 5.0

Abstract

With cutting-edge technology and data-driven insights, intelligent logistics has become a game-changing supply chain management strategy, helping maximize productivity and streamline processes. The main ideas of smart logistics are outlined in this study. It includes several factors that companies must consider while assessing and implementing intelligent logistics solutions. Real-time visibility, supply chain collaboration, inventory management, robotics and automation, supply chain integration, predictive analytics, scalability and flexibility, security and data privacy, and return on investment are some requirements. Using these criteria, organizations may use intelligent logistics to optimize inventory levels, boost customer happiness, save costs, simplify procedures, and enhance supply chain efficiency. In a dynamic and connected world, intelligent logistics solutions help businesses make data-driven choices, react fast to market needs, and adjust to changing business situations. We applied machine learning algorithms to analyze the supply chain dataset. We used two machine learning algorithms: a decision tree and a random forest. We compute the accuracy, precision, recall, and f1 score. The decision tree has the highest accuracy, with 91%. Then, we used the multi-criteria decision-making (MCDM) methodology to analyze the criteria of intelligent logistics. The AHP method is used to compute the weights of criteria.